Human atlas is an important tool for understanding brain. It can help us understand the relationship between brain function and structure.It is also helpful for clinical. With the advancement of magnetic resonance technology in recent years, more and more fine-grained brain atlas have been established. However, most atlas tends to focus on the reliability of the parcellations in the population while ignores the impact of individual differences on brain regions.On the other hand, due to the limited individual data and the lack of gold standard for individual parcellation, the precision of individual-level atlas is limited. Aiming at this situation, this thesis systematically investigated the method of individual-level brain mapping based on structural connectivity.It also gives a quantitative evaluation scheme for various aspects of subject-specific atlas. The main contents of this paper are as follows:

We performed individual parcellation method based on structural connectivity with diffusion tensor imaging. The pipeline drawed on the traditional connectivity-based parcellation method and employed the surface-based probabilistic tracking algorithm to obtain the structural connectivity fingerprint of the vertex, which was used as a basis to parcellate the brain. With no standard-gold for individual brain atlas, we combined the data and information provided by the Brainnetome atlas to transform unsupervised problem into a typical supervised learning problem. We chose LightGBM, an algorithmic framework based on gradient boost trees, as areal classifiers that distinguished each subregion.We trained areal classifiers with high-quality data to learn the structural connectivity fingerprint of each brain region, and employed trained areal classifier for parcellations of new comes. We found that regional classifiers achieve high accuracy and have enough discriminate ability between different brain regions. Although our model consumed a lot of time to train, the trained regional classifier can directly invest in the application scenario of individual parcellation with smooth post-processing.The subject-specific brain atlas was obtained efficiently and accurately with areal classifiers.

As individual-level atlas has no gold standard, assessing the quality of individualized parcellations has always been a problem. Therefore, this paper hopes to establish a comprehensive evaluation pipeline to validate the reliability and robustness of the individual-level atlas. In this paper, the results of the magnetic resonance data collected by 39 subjects with two sessions were tested. We found that our parcellation results were reproducible while retained differences cross subjects. We also calculated the spatial distribution of inter-subject variability in different brain regions. We found that the results were consistent with intuition that the primary cortex is more stable than association cortex. In addition, dissimilarity of structural connectivity of the brain regions was computed. We found that the individual-level brain maps were more representative of the functional specificity of each brain region than the group-level maps.